In this article we envision how an asset might be managed in the future.

There is a lot of interest in digital oil fields these days. But there is also a lot of confusion: What is a digital oil field? What are the advantages? How do we get there? To answer these questions, consider the following scenario:

Renée is an asset team leader for a global oil company. Her asset is a modern one, even for the digital world. The first thing she does in the morning is check a TV monitor hung on the wall of her kitchen for messages. In addition to news and weather, one window displays messages from her company.

This morning, she notices an exception report from the field - apparently well AP 47 is producing below expectations. It's been low for several months, but last night it dropped below the set point for an exception alert. The set point was determined from a model of the field, one which includes a reservoir model based on geophysical, geological, petrophysical data, and models of the surface facilities and pipelines.

Renée can think of several possible causes for the production decline, and decides she needs more information. She uses her secure access to the Internet to shut the gas lift valve on 47 and enable an automatic pressure buildup analysis (PBU). That data, she thinks, will be helpful later in the day as she works with her team to identify the cause. She also e-mails her geophysicist, Vik, giving him advance notice that they will need to discuss a probable fault that he had mentioned last week.

On the way to work, she drives her son, Bobby, to school. "Did you do your homework?" she asks (a little late). Yes, he had. The homework had been submitted wirelessly to the school computer and his grades returned overnight on his "Kid PDA." He proudly shows his mother the 92 he had earned - an "A," but a borderline one. She had time to make sure he understood his mistakes before she dropped him off at school.

Still in the car, Renée places a call to Lin, a field-based coworker with extensive experience. She asks him if there is anything about the compressor performance or the gas lift system that would explain the declining production from well 47. He promises to get back to her by the end of his workday (Lin is 6 hours to the east).

At work, Renée reruns the reservoir simulator, looking for possible causes that could explain the problem. She tried putting a skin on the well, reducing the permeability in the area of well 47 and putting a partially sealing fault halfway to the neighboring well. With enough difference, any of those changes could explain the deviant behavior. But by mid-morning she had learned enough to calibrate the information she would be gathering during the day from her teammates.

Later that morning, she hears from Lin. The compressor is nearing time for preventive maintenance, but he doesn't see anything that could explain the drop in production. He also couldn't find a problem with the gas lift equipment.

Vik calls with a status report. He requests another day to reprocess the seismic data. Dr. Amar from corporate research, who is 10 time zones away, has recently made some improvements in the migration routines and has recommended some slight changes in seismic velocities for unconsolidated rocks such as they have. Vik wants to take a careful look at several routines to see if one might help identify the suspected fault.

After lunch, she meets with her asset team, which is scattered around the globe. She describes the problem with well 47 and uses an online collaboration tool to show them the production history and model results. She also describes the sensitivity studies from her simulation work earlier in the day.

"Any suggestions?" she asks.

Together they brainstorm possible causes - increased formation damage, well damage from tubing collapse or plugging with scale or wax, or gas lift problems with valves or flow lines. Or, it could be the simulator was not modeling the reservoir correctly - too high a permeability, incorrect reservoir continuity, or a missing fault.

Together they develop an action plan. Vik will reprocess the seismic data. Marion, the team's production engineer, will run a nodal analysis to include the well, surface facilities and the pipeline to shore. Jack will look at log data (once again) to see if some explanation might appear. Renée also asks her business analyst, Juan, to prepare a cash flow impact analysis using the corporate performance management (CPM) tool.

By now, the results of the pressure buildup (PBU) are available (a pressure buildup is a well-established technique for measuring the productivity of the reservoir and of the well). Graphs of kh (the product of permeability and thickness, or reservoir productivity) and skin (a measure of flow impairment in the well) over time, derived from this morning's shut-in and from previous flow interruptions, show a gradual increase in skin, but not enough to explain the production performance. Renée's sensitivity runs earlier in the day help rule this out as a sufficient cause.

By the next morning the answer is clear. Marion's nodal analysis has turned up some suspicious pressures in the gas distribution system. With that data, Lin quickly finds the problem. An orifice meter has become partially plugged with debris. Sensing high-pressure drops, the gas allocation algorithm partially closed the gas valve to well 47, thus reducing lift and production.

To verify that this is a sufficient cause to explain the problem, Renée calls Ramón in information technology and asks if he can run a test case with the gas lift allocation software. Together they watch the results in the team room. With the software in test mode Ramón artificially reduces the orifice size to see what happens. Voila! The results easily match actual data.

By late morning, the orifice plate is replaced and production is back up, in agreement with the model.

That afternoon, a congratulatory note from her management to the team puts a smile on Renée's face.

In this scenario, the problem is identified and solved in a little over a day. It is identified by an exception report, which focuses the team's efforts on a specific problem. The team is global, working almost 24/7 and able to interact with each other and with the field over the Internet to get data, to collaborate or to make operational changes. As a result, the field is operated much closer to optimum, with increased production and profitability for the company.

Hopefully, this scenario helps define a digital oil field and helps us grasp its potential. But the question still remains: How do we get there?

The three components - people, processes and technology - have to come together to make the digital oilfield concept to reality.

The first challenge is technology. New hardware and software are needed. Step one is to establish connectivity - the ability to communicate among wells, fields and team members globally. Step two is to build software that automates and optimizes field operations. Next, the capability to operate fields remotely must be established. Also, the industry must develop software that compares actual results to forecasts, and creates and communicates exception reports when the two differ beyond set parameters. Finally, the impact on financial analysis must be continuously assessed and reported. This is all possible with today's technology, but much remains to be developed. Justifying the development and implementation of this technology remains a challenge for most of us.

Work processes need to give global teams the ability to gather data, make quick decisions and change operations quickly. The asset team in this scenario identified and solved the well problem without intervention from, or even consulting with, company management. So, where does management fit in and how do they assure the company's assets are well managed? And, what is the role of operations? How will safety issues be addressed? There are some very important questions here. Many changes in work processes need to be addressed and changed in determining how work gets done in this scenario.

Empowering people to take action - collect data, commission work by the team and implement solutions - brings faster resolution to production problems. Company culture needs to allow and encourage this. Imagine what happens the first time a vice president calls Renée to criticize an action she took. "I would have done that differently!" the vice president says. From that point forward, Renée will not take action without first consulting her boss. Many e-mails and several days later, she and her supervisor will have secured the necessary authorizations to shut in well 47 to get a PBU. Identifying and solving the problems will take weeks, rather than a day-and-a-half. In the meantime, production and profits are lost. Establishing a supportive corporate culture will take real effort. Training and knowledge management are also important considerations. If Renée and her team are to be empowered, as this scenario suggests, they will need ready access to best practices and past learnings.

In a world of digital oil fields, Renée plays an important role in the company and gets rewarded frequently for resolving problems quickly. She loves her job, and even though the job is always with her - at home, in the car and at the office - she has plenty of time to take care of her other important roles - that of mother and wife.

And she wouldn't trade any of her roles with anyone.